A first-principles and machine learning combined method to investigate the interfacial friction between corrugated graphene

Modelling and Simulation in Materials Science and Engineering, vol. 29, pp. 035011

Abstract

Abstract Simulating the frictional properties of complex interfaces is computational resource consuming. In this paper, we propose a density functional theory (DFT) calculation combined machine learning (ML) strategy to investigate the sliding potential energy corrugation between geometrical corrugated graphene (Gr) sheets. By the aid of few DFT calculations and geometrical descriptors Σ r − n ( n = 1, 2, 6, 12), the trained ML models can accurately predict the sliding potential evolutions of Gr/Pt and Gr/Re systems. To be specific, based on DFT calculations of sliding along [110] direction, the trained linear regression (LIN) models can properly give out the potential energy evolution along the [100] direction with deviation less than 5%. By the dataset of given distances (9.3 Å, 9.65 Å and 10 Å) between two Re monolayers in Gr/Re systems, LIN and Bayesian ridge regression (BR) models can quantitatively predict the potential energy evolution of unknown distances (9.2 Å, 9.4 Å, 9.5 Å and 9.6 Å). The predicted magnitudes of potential energy corrugations by BR model divert less than 3 meV Å −2 from DFT calculations. The prediction results for extrapolated distances (9.0 Å and 9.1 Å) deviate notably, but the extension of training dataset effectively improves the predictive ability of ML models, especially for the LIN model. Thus, the supposed strategy could become an effective method to investigate the frictional characteristics of complex interfaces.

Authors 9

1. Zugang Liu corresponding

   University of Science and Technology Beijing

   Affiliation as printed

   Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China

2. Xinpeng Zhao corresponding

   University of Science and Technology Beijing

   Affiliation as printed

   Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China

3. Heyuan Wang corresponding Aachen Institute for Materials Applications in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Materials Applications in Mechanical Engineering, RWTH Aachen University, Aachen 52062, Germany

   Institute for Materials Applications in Mechanical Engineering, RWTH Aachen University, Aachen, 52062, Germany

4. Yuan Ma corresponding

   University of Science and Technology Beijing

   Affiliation as printed

   Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China

5. Lei Gao corresponding

   University of Science and Technology Beijing

   Affiliation as printed

   Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China

6. Haiyou Huang corresponding

   University of Science and Technology Beijing

   Affiliation as printed

   Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China

7. Yu Jing Yan corresponding

   University of Science and Technology Beijing

   Affiliation as printed

   Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China

8. Yanjing Su corresponding

   University of Science and Technology Beijing

   Affiliation as printed

   Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China

9. Lijie J. Qiao corresponding

   University of Science and Technology Beijing

   Affiliation as printed

   Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, People’s Republic of China